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</head><body><div id="sidebar"><div id="menu-mask"></div><div id="sidebar-menus"><div class="author-avatar"><img class="avatar-img" src="/img/avatar.png" onerror="onerror=null;src='/img/friend_404.gif'" alt="avatar"/></div><div class="site-data"><div class="data-item is-center"><div class="data-item-link"><a href="/archives/"><div class="headline">文章</div><div class="length-num">46</div></a></div></div><div class="data-item is-center"><div class="data-item-link"><a href="/tags/"><div class="headline">标签</div><div class="length-num">19</div></a></div></div><div class="data-item is-center"><div class="data-item-link"><a href="/categories/"><div class="headline">分类</div><div class="length-num">7</div></a></div></div></div><hr/><div class="menus_items"><div class="menus_item"><a class="site-page" href="/"><i class="fa-fw fas fa-home"></i><span> 首页</span></a></div><div class="menus_item"><a class="site-page" href="/archives/"><i class="fa-fw fas fa-archive"></i><span> 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id="page-header" style="background-image: url('https://gitee.com/wrj1006er/pic/raw/master/pic/07174a74ea4b6c741b57ab39a26c5fd112c3c0b5.jpg@942w_531h_progressive.webp')"><nav id="nav"><span id="blog_name"><a id="site-name" href="/">1006er的博客</a></span><div id="menus"><div class="menus_items"><div class="menus_item"><a class="site-page" href="/"><i class="fa-fw fas fa-home"></i><span> 首页</span></a></div><div class="menus_item"><a class="site-page" href="/archives/"><i class="fa-fw fas fa-archive"></i><span> 归档</span></a></div><div class="menus_item"><a class="site-page" href="/tags/"><i class="fa-fw fas fa-tags"></i><span> 标签</span></a></div><div class="menus_item"><a class="site-page" href="/categories/"><i class="fa-fw fas fa-folder-open"></i><span> 分类</span></a></div><div class="menus_item"><a class="site-page" href="javascript:void(0);"><i class="fa-fw fas fa-list"></i><span> 清单</span><i class="fas fa-chevron-down expand"></i></a><ul class="menus_item_child"><li><a class="site-page" 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class="post-meta-label">更新于</span><time class="post-meta-date-updated" datetime="2021-11-11T10:56:55.462Z" title="更新于 2021-11-11 18:56:55">2021-11-11</time></span><span class="post-meta-categories"><span class="post-meta-separator">|</span><i class="fas fa-inbox fa-fw post-meta-icon"></i><a class="post-meta-categories" href="/categories/%E8%81%94%E9%82%A6%E5%AD%A6%E4%B9%A0/">联邦学习</a></span></div><div class="meta-secondline"><span class="post-meta-separator">|</span><span class="post-meta-pv-cv"><i class="far fa-eye fa-fw post-meta-icon"></i><span class="post-meta-label">阅读量:</span><span id="busuanzi_value_page_pv"></span></span></div></div></div></header><main class="layout" id="content-inner"><div id="post"><article class="post-content" id="article-container"><h1 id="联邦学习论文笔记-2"><a href="#联邦学习论文笔记-2" class="headerlink" title="联邦学习论文笔记(2)"></a>联邦学习论文笔记(2)</h1><h2 id="《FedFast-Going-Beyond-Average-for-Faster-Training-of-Federated-》"><a href="#《FedFast-Going-Beyond-Average-for-Faster-Training-of-Federated-》" class="headerlink" title="《FedFast: Going Beyond Average for Faster Training of Federated 》"></a>《FedFast: Going Beyond Average for Faster Training of Federated 》</h2><h3 id="研究背景"><a href="#研究背景" class="headerlink" title="研究背景"></a>研究背景</h3><p>本文的重点就是改进原来基础的FedAvg算法存在的推荐效果差和收敛速度慢所导致的通信轮次过多和客户端通信资源浪费的问题。</p>
<h3 id="架构"><a href="#架构" class="headerlink" title="架构"></a>架构</h3><p><img src="https://gitee.com/wrj1006er/pic/raw/master/pic/9.png" alt="RUNOOB 图标"></p>
<p>系统中存在几个角色：中心服务器、客户端、用户和推荐物</p>
<h3 id="FedAvg"><a href="#FedAvg" class="headerlink" title="FedAvg"></a>FedAvg</h3><p>在FedAvg算法中每个轮次在选择客户端的时候采用随机的方式进行选择，并且在一轮迭代完成中心服务器获得所选的客户端的训练的参数的时候采用的是求平均的方式整合获得完整的模型参数</p>
<h3 id="FedFast"><a href="#FedFast" class="headerlink" title="FedFast"></a>FedFast</h3><p>根据上面的介绍我们可以知道FedAvg 算法在选择用户的时候采用随机过程但是仔细想想在实际生活中用户与用户之间很有可能具有相似的兴趣，那么对于具有相似兴趣的用户群中的两个用户来说在采样的时候只需要选择其中的一个就可以了，这样就可以降低迭代轮次。具体来说FedFast主要改进系统的两个方面，<br>1.ActvSAMP用于更好地选择参与每轮培训的用户<br>2.ActvAGG以智能方式组合本地培训的模型，以加快培训过程。</p>
<p><img src="https://gitee.com/wrj1006er/pic/raw/master/pic/10.png" alt="RUNOOB 图标"></p>
<p>FedFast伪代码</p>
<ol>
<li>初始化权重和客户群分布，这里作者提到初始客户群分布的获取是对用户嵌入集合做聚类实现的，并且客户群分布的获取是在中心服务器中进行的。</li>
<li>从p个用户集合中总计取出m个客户端，并保证不同客户集合中取出的客户端数量相等。在一个客户集合内部采用随机的方式取出。</li>
<li>使用取出的客户端的数据分布式的学习，学习之后将学习的权重发送至中心服务器。</li>
<li>中心服务器整合收到的客户端学习权重和参数使用ActvAGG 算法获得完整模型参数并发送给所有客户端。</li>
<li>客户端更新自己的模型参数。</li>
<li>2-5过程称为一个轮次，重复多个轮次直至收敛</li>
</ol>
<p><img src="https://gitee.com/wrj1006er/pic/raw/master/pic/11.png" alt="RUNOOB 图标"></p>
<p>ActvSAMP伪代码</p>
<p>在每个用户群中提取样本一共提取m个</p>
<p><img src="https://gitee.com/wrj1006er/pic/raw/master/pic/12.png" alt="RUNOOB 图标"></p>
<p>ActvAGG算法伪代码</p>
<p>对于模型来说主要有三部分的参数需要更新，分别是：Item嵌入、user嵌入、模型用到的权重（weight)矩阵。在FedAvg中的更新内容就只是更新权重矩阵并且只是求一个均值即可。</p>
<p><img src="https://gitee.com/wrj1006er/pic/raw/master/pic/13.png" alt="RUNOOB 图标"></p>
<p>非嵌入参数（N）的更新（求和取平均）</p>
<p><img src="https://gitee.com/wrj1006er/pic/raw/master/pic/14.png" alt="RUNOOB 图标"></p>
<p>item嵌入的更新</p>
<p><img src="https://gitee.com/wrj1006er/pic/raw/master/pic/16.png" alt="RUNOOB 图标"></p>
<p>代表用户嵌入的更新（就是被选择的用户）<br>对于代表用户直接采用这一轮次的值，没有改变</p>
<p><img src="https://gitee.com/wrj1006er/pic/raw/master/pic/17.png" alt="RUNOOB 图标"></p>
<p>从属用户嵌入的更新（就是用户分区中没有被选择的用户）与本用户分区内部的代表的嵌入有关。<br>从属用户嵌入的值是原来的值加上一个折扣率乘以本客户群内部所有代表用户嵌入的平均改变值的和。</p>
<ol>
<li>计算本分区内所有代表本轮的嵌入和上一轮嵌入的差值的和 δ[Us]</li>
<li>计算上面插值的平均值</li>
<li>上面的插值乘以折扣率γ，γ的计算方式是一个指数函数并随着轮次的增大而减小：</li>
<li>对于从属用户新的嵌入是上面获得的值加上上一轮的值从而获得新的嵌入。</li>
</ol>
<h3 id="实验结果"><a href="#实验结果" class="headerlink" title="实验结果"></a>实验结果</h3><h4 id="准确率"><a href="#准确率" class="headerlink" title="准确率"></a>准确率</h4><p><img src="https://gitee.com/wrj1006er/pic/raw/master/pic/18.png" alt="RUNOOB 图标"></p>
<ol>
<li>准确率相较于联邦学习FedAvg方法有较大提升。</li>
<li>相比于集中学习方法在一些数据集上的效果更好。</li>
</ol>
<h4 id="训练轮次"><a href="#训练轮次" class="headerlink" title="训练轮次"></a>训练轮次</h4><p><img src="https://gitee.com/wrj1006er/pic/raw/master/pic/19.png" alt="RUNOOB 图标"></p>
<p>相比与传统的FedAvg方法的收敛的速度更快。</p>
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class="toc-text">研究背景</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#%E6%9E%B6%E6%9E%84"><span class="toc-number">1.1.2.</span> <span class="toc-text">架构</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#FedAvg"><span class="toc-number">1.1.3.</span> <span class="toc-text">FedAvg</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#FedFast"><span class="toc-number">1.1.4.</span> <span class="toc-text">FedFast</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#%E5%AE%9E%E9%AA%8C%E7%BB%93%E6%9E%9C"><span class="toc-number">1.1.5.</span> <span class="toc-text">实验结果</span></a><ol class="toc-child"><li class="toc-item toc-level-4"><a class="toc-link" href="#%E5%87%86%E7%A1%AE%E7%8E%87"><span class="toc-number">1.1.5.1.</span> <span class="toc-text">准确率</span></a></li><li class="toc-item toc-level-4"><a class="toc-link" href="#%E8%AE%AD%E7%BB%83%E8%BD%AE%E6%AC%A1"><span 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